Exploration Of Spatial Distribution Of Brain Metastasis From Small Cell Lung Cancer And Screening Of High-Risk And Low-Risk Structural Regions

2020 ◽  
Vol 108 (3) ◽  
pp. e707-e708
Author(s):  
Y. Wang ◽  
W. Xia ◽  
X. Gao ◽  
S. Yuan
2021 ◽  
Vol 8 ◽  
Author(s):  
Lei-Lei Wu ◽  
Wu-Tao Chen ◽  
Xuan Liu ◽  
Wen-Mei Jiang ◽  
Yang-Yu Huang ◽  
...  

Background: In this study, we aim to establish a nomogram to predict the prognosis of non-small cell lung cancer (NSCLC) patients with stage I-IIIB disease after pneumonectomy.Methods: Patients selected from the Surveillance, Epidemiology, and End Results (SEER, N = 2,373) database were divided into two cohorts, namely a training cohort (SEER-T, N = 1,196) and an internal validation cohort (SEER-V, N = 1,177). Two cohorts were dichotomized into low- and high-risk subgroups by the optimal risk prognostic score (PS). The model was validated by indices of concordance (C-index) and calibration plots. Kaplan-Meier analysis and the log-rank tests were used to compare survival curves between the groups. The primary observational endpoint was cancer-specific survival (CSS).Results: The nomogram comprised six factors as independent prognostic indictors; it significantly distinguished between low- and high-risk groups (all P < 0.05). The unadjusted 5-year CSS rates of high-risk and low-risk groups were 33 and 60% (SEER-T), 34 and 55% (SEER-V), respectively; the C-index of this nomogram in predicting CSS was higher than that in the 8th TNM staging system (SEER-T, 0.629 vs. 0.584, P < 0.001; SEER-V, 0.609 vs. 0.576, P < 0.001). In addition, the PS might be a significant negative indictor on CSS of patients with white patients [unadjusted hazard ration (HR) 1.008, P < 0.001], black patients (unadjusted HR 1.007, P < 0.001), and Asian or Pacific Islander (unadjusted HR 1.008, P = 0.008). In cases with squamous cell carcinoma (unadjusted HR 1.008, P < 0.001) or adenocarcinoma (unadjusted HR 1.008, P < 0.001), PS also might be a significant risk factor.Conclusions: For post-pneumonectomy NSCLC patients, the nomogram may predict their survival with acceptable accuracy and further distinguish high-risk patients from low-risk patients.


2021 ◽  
Vol 12 ◽  
Author(s):  
Yang Teng ◽  
Bo Wang ◽  
Desi Shang ◽  
Ning Yang

Background: Non–small cell lung cancer (NSCLC) is among the major health problems around the world. Reliable biomarkers for NSCLC are still needed in clinical practice. We aimed to develop a novel ferroptosis- and immune-based index for NSCLC.Methods: The training and testing datasets were obtained from TCGA and GEO databases, respectively. Immune- and ferroptosis-related genes were identified and used to establish a prognostic model. Then, the prognostic and therapeutic potential of the established index was evaluated.Results: Intimate interaction of immune genes with ferroptosis genes was observed. A total of 32 prognosis-related signatures were selected to develop a predictive model for NSCLC using LASSO Cox regression. Patients were classified into the high- and low-risk group based on the risk score. Patients in the low-risk group have better OS in contrast with that in the high-risk group in independent verification datasets. Besides, patients with a high risk score have shorter OS in all subgroups (T, N, and M0 subgroups) and pathological stages (stage I, II, and III). The risk score was positively associated with Immune Score, Stromal Score, and Ferroptosis Score in TCGA and GEO cohorts. A differential immune cell infiltration between the high-risk and the low-risk groups was also observed. Finally, we explored the significance of our model in tumor-related pathways, and different enrichment levels in the therapeutic pathway were observed between the high- and low-risk groups.Conclusion: The present study developed an immune and ferroptosis-combined index for the prognosis of NSCLC.


BMJ Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. e043234
Author(s):  
Atsushi Kagimoto ◽  
Yasuhiro Tsutani ◽  
Takahiro Mimae ◽  
Yoshihiro Miyata ◽  
Norihiko Ikeda ◽  
...  

IntroductionRecently, inhibition of programmed cell death 1 or its ligand has shown therapeutic effects on non-small cell lung cancer (NSCLC). However, the effectiveness of preoperative nivolumab monotherapy for stage I NSCLC remains unknown. The present study aimed to investigate the pathological response of preoperative treatment with nivolumab for clinically node negative but having a high risk of NSCLC recurrence.Methods and analysisThe Preoperative Nivolumab (Opdivo) to evaluate pathologic response in patients with stage I non-small cell lung cancer: a phase 2 trial (POTENTIAL) study is a multicentre phase II trial investigating efficacy of preoperative nivolumab for clinical stage I patients at high risk of recurrence. This study includes histologically or cytologically confirmed NSCLC patients with clinical N0 who were found on preoperative high-resolution CT to have a pure solid tumour without a ground-glass opacity component (clinical T1b, T1c or T2a) or a solid component measuring 2–4 cm in size (clinical T1c or T2a). Patients with epidermal growth factor receptor (EGFR) mutation (deletion of exon 19 or point mutation on exon21, L858R), anaplastic lymphoma kinase (ALK) translocation or c-ros oncogene 1 (ROS-1) translocation are excluded from this study. Nivolumab (240 mg/body) is administrated intravenously as preoperative therapy every 2 weeks for three cycles. Afterward, lobectomy and mediastinal lymph node dissection (ND 2a-1 or ND 2a-2) are performed. The primary endpoint is a pathological complete response in the resected specimens. The secondary endpoints are safety, response rates and major pathological response. The planed sample size is 50 patients. Patients have been enrolled since April 2019.Ethics and disseminationThis trial was approved by the Institutional Review Board of Hiroshima University Hospital and other participating institutions. This trial will help examine the efficacy of preoperative nivolumab therapy for clinical stage I NSCLC.Trial registration numberjRCT2061180016.


2021 ◽  
Vol 3 (Supplement_3) ◽  
pp. iii15-iii16
Author(s):  
Raees Tonse ◽  
Muni Rubens ◽  
Haley Appel ◽  
Martin C Tom ◽  
Matthew D Hall ◽  
...  

Abstract Background Novel immunotherapeutic strategies, such as those targeting the PD-1/PD-L1 axis, are promising in patients with metastatic lung cancer and are often administered when tumors show PD-L1 positivity. The objective of this study was to analyze PD-L1 receptor discordance in tumor cell between the primary tumor and lung cancer brain metastasis (LCBM). Methods A systematic review of series published prior to April 2021 obtained from the Medline database of biopsied or resected LCBM evaluating PD-L1 discordance was performed using PRISMA guidelines. Weighted random effects models were used to calculate pooled estimates. Results Six full-text articles (n=247 patients) with a median of 32 patients in each study (range: 24–73 patients) reported PD-L1 receptor expression analyses of both primary lung tumors and brain metastases. The majority of patients (81%) were smokers, with 67% non-small cell lung cancer and 33% small cell lung cancer. The pooled estimate for overall PD-LI receptor concordance between primary and LCBM was 76% (95% CI: 52%-90). The positivity rate varied when analyzed by various cutoff levels of PD-L1 expression; for <1% expression, it was 41% (95% CI: 22%-62%) for primary vs. 58% (95% CI: 35%-78%) for LCBM; for PD-L1 expression of 1–50%, it was 24% (95% CI: 13%-40%) vs. 19% (95% CI: 10%-33%); and for PD-L1 >50% it was 12% (95% CI: 4%-33%) vs. 21% (95% CI: 14%-29%) (p=0.425). The pooled estimate for overall PD-LI receptor discordance between primary and LCBM was 17% (95% CI: 10%-27%). Meta-regression analysis showed that age, sex, smoking status, and histology were not associated with PD-LI receptor discordance. Conclusions PD-L1 status discordance in tumor cell occurs in approximately 20% of LCBM, with the greatest discordance in the <1% expression category. Awareness of this discordance is important for the selection of immune checkpoint inhibitor therapy as well as in the analysis of patterns of failures.


2017 ◽  
Vol 18 (1) ◽  
pp. 11-13 ◽  
Author(s):  
Ufuk Yilmaz ◽  
Esra Korkmaz Kirakli ◽  
Umit Gurlek ◽  
Yasemin Ozdogan ◽  
Bahri Gumus ◽  
...  

2019 ◽  
Vol 108 (3) ◽  
pp. 568-574
Author(s):  
Hidetoshi Yanai ◽  
Kai Kawashima ◽  
Kai Yazaki ◽  
Takeshi Numata ◽  
Kyoko Ota ◽  
...  

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